Design, develop, and deploy AI and machine learning solutions tailored to retail challenges such as personalized product recommendations, dynamic pricing, and demand forecasting.
Collaborate with data scientists, product managers, engineers, and retail analysts to develop AI-driven features that improve customer experience and operational efficiency.
Build and manage data pipelines that support large-scale training and inference workloads using structured and semi-structured retail data.
Develop and optimize deep learning models using TensorFlow or PyTorch for applications like visual product search, customer segmentation, and chatbot automation.
Integrate AI models into customer-facing platforms (e.g., mobile apps, websites) and backend retail systems (e.g., inventory management, logistics).
Monitor model performance post-deployment and implement continuous improvement strategies based on business KPIs and real-time data.
Contribute to model governance, testing, and documentation to ensure models are fair, explainable, and secure.
Stay informed about AI trends in the retail and e-commerce industry to help the team stay competitive and innovative.

Required Skills & Experience:

AI/ML Expertise :

Minimum of 3 years of experience in developing and deploying machine learning models in production.
Proficiency in Python and common ML libraries such as TensorFlow, PyTorch, Scikit-learn, and XGBoost.
Strong understanding of supervised/unsupervised learning, model evaluation, and feature engineering.

Retail & Data Integration:

Minimum of 2 years working with backend systems or data integration workflows in a commercial or retail setting.
Experience working with transactional data, product catalogs, customer behavior data, and retail KPIs.
Familiarity with RESTful API integration and deployment of ML services using Flask, FastAPI, or similar frameworks.
Proficiency in SQL and working with relational databases (e.g., PostgreSQL, BigQuery) and data warehouses.

Cloud & Infrastructure:

Hands-on experience with cloud services (GCP preferred).
Experience with containerization (Docker) and orchestration (Kubernetes) for deploying scalable AI services.
Familiarity with MLOps tools and workflows (e.g., MLflow, Airflow).

General:

Strong analytical and problem-solving skills with the ability to translate business problems into technical solutions.
Comfortable working in Agile teams and collaborating across technical and non-technical functions.
Strong written and verbal communication skills.

Preferred Qualifications:

Experience in retail, e-commerce, or home improvement product domains.
Familiarity with recommendation algorithms (collaborative filtering, content-based filtering).
Exposure to computer vision (e.g., for product tagging, image search) and NLP (e.g., for intelligent customer support or search optimization).
Experience with CI/CD pipelines and AI model lifecycle management.
Experience with real-world ML applications in retail such as recommendation systems, demand forecasting, inventory optimization, or customer segmentation.
Master s degree in Computer Science, Data Science, Engineering, or a related field.

AI Engineer